Clouds–SST relationship and interannual variability modes of Indian summer monsoon in the context of clouds and SSTs: observational and modelling aspects. (9th February 2016) - Chaudhari, Hemantkumar S.; Pokhrel, Samir; Kulkarni, Ajay; Hazra, Anupam; Saha, Subodh Kumar
ABSTRACT: This study examines the relationship between clouds and sea surface temperatures (SSTs) during Indian Summer Monsoon (ISM). Observation reveals dominance of high‐level clouds in the monsoon region. NCEP Climate Forecast System version 2 (CFSv2) is able to replicate the observed high‐level cloud fractions although it underestimates the magnitude. Cloud–SST relationship for observation depicts dominance of positive correlation over equatorial Indian Ocean region which is well recapitulated in CFSv2 simulations. To investigate the most dominating patterns of interannual variability of rainfall, SST and clouds, empirical orthogonal function (EOF) analysis is performed on seasonal JJAS (June–September) dataset. EOF analysis signifies that high‐level clouds are highly correlated with rainfall for observations during ISM season. This study has also investigated relationship of clouds and El Niño Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) during ISM period. Interannual variations of clouds connote significant correlation with ENSO index (Nino 3/Nino 3.4). First principal component (PC1) of high‐level clouds and ENSO index indicate significant negative correlation. EOF analysis based on observation also connotes that first mode (second) of EOF analysis is associated with ENSO (IOD). It is also confirmed by maximum covariance analysis (MCA). CFSv2 is also able to depict the significant negative correlation between PC1 of high‐level clouds and ENSO index. Observation based second principal component (PC2) of high‐level clouds and IOD index exhibits significant positive correlation. It gives indication that PC2 of observed high‐level clouds can be associated with IOD. In contrast, PC2 of CFSv2 simulated high‐level cloud and IOD index are not correlated. EOF analysis based on CFSv2 shows that the first mode of EOF analysis is associated with ENSO; however, second mode of EOF is not related with IOD. MCA analysis also supports these findings. It means that CFSv2 has good ability to represent ENSO as compared with IOD.